datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
coding_agent_tracesWe release coding agent traces using Claude Code for
Opus
ISL, OSL, ISL_new counts
GPT-oss-120B
ISL, OSL, ISL_new counts and their raw texts
For Opus, only the locally saved files from the harness were used for analysis.
Coding agents take multiple turns to carry out a task from the input prompt. To analyze the token distribution, two models were selected: Anthropic's Opus and OpenAI's gpt-oss-120B. The input sequence length (ISL), output sequence length (OSL) and the uncached, new input… See the full description on the dataset page: https://huggingface.co/datasets/netpreme/coding_agent_traces.Agentic-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data Structure… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset.SI2CA-Training-TrajectoriesDataset Card for SI2CA-Training-Trajectories
[🌐 Website] •
[🤗 Dataset] •
[📜 Paper] •
[🐱 GitHub]
💡 Introduction
This dataset consists of 32,340 coding-agent trajectories generated by Qwen3.5-122B-A10B on the same 10,780 executable Python SWE tasks under the three trajectory-curation settings of Section 4.4 of the paper: standard sampling, full self-judgement, and an efficient discovered strategy found by the recursive self-improvement framework. Each task is… See the full description on the dataset page: https://huggingface.co/datasets/Self-Improving-Coding-Agents/SI2CA-Training-Trajectories.agentic-coding-trajectories-grok46
Agentic Coding Trajectories (Grok 4.6)
Rights & intended use: public research corpus, not training data.
Hosted frontier-model outputs are research-only inputs under project policy
(synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json. License:
Synthetic Factory Research-Only License v1.0 (license: other, see LICENSE) (non-commercial).
Release status: the raw… See the full description on the dataset page: https://huggingface.co/datasets/rmems/agentic-coding-trajectories-grok46.guidellm-agentic-coding-trajectories
GuideLLM agentic coding trajectories
A sampled serving-load benchmark derived from Thoughtworks agentic-coding-trajectories, for GuideLLM and an OpenAI-compatible /v1/chat/completions endpoint. There are 630 rows representing 481 unique source sessions, across the same 8turn, 24turn, and 48turn configurations as the earlier version.
The configuration names now refer to original logical steps, not always HTTP request counts. Native tool steps expand into a tool-call request and a… See the full description on the dataset page: https://huggingface.co/datasets/zetomatoz/guidellm-agentic-coding-trajectories.agentic-coding-trajectories
agentic-coding-trajectories
A unified, tokenized corpus of 15,000 multi-turn agentic-coding sessions (618K turns, 41 turns/session avg) drawn from three publicly-released upstream datasets. Built for benchmarking LLM serving systems on realistic multi-turn coding-agent workloads.
Why this exists
Most LLM serving benchmarks use single-shot prompts. Real coding agents work in long multi-turn loops where each turn appends to a growing prompt. This corpus captures that shape… See the full description on the dataset page: https://huggingface.co/datasets/thoughtworks/agentic-coding-trajectories.agentic-coding-trajectories
Agentic Coding Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated coding-episode payload is published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/agentic-coding-trajectories.coding-agent-security-benchmark
Coding Agent Security Benchmark
A benchmark for evaluating whether an LLM can correctly identify security
violations in the behavior of an autonomous coding agent - spanning
dangerous shell commands, credential leakage, prompt injection, supply-chain
risk, privacy leaks, and more.
Each row is a single message sampled from a coding-agent session (a user
instruction, a tool call the agent issued, a tool's response, or the agent's
own output) paired with a ground-truth security… See the full description on the dataset page: https://huggingface.co/datasets/rogue-security/coding-agent-security-benchmark.agentic-coding-tracesThis dataset is aggregated from:
"AletheiaResearch/GLM-5.2-Agent",
"armand0e/claude-fable-5-claude-code",
"armand0e/minimax-m3-claude-code-traces",
"AletheiaResearch/Kimi-K3-Codex"
and has been converted into a single unified format:
{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "<think>...</think>..."}, ...]}
The original raw files are kept in the .raw_sources folder for reference if you'd like to take a closer look.
Main file: USE_THIS_train.jsonl
ty
tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified
Text to Terminal, v2 — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned, combined, and thinking-augmented version of muellerzr/text_to_terminal_v2. It pairs natural language instructions with their corresponding terminal/bash commands, now augmented with explicit <think> reasoning traces that model the step-by-step thought process before producing the final command.The restructuring approach is directly… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified.Hunter-Alpha-Coding-Agent-SFTcontext-ucurve-coding-agents
Context U-curve: 36 coding-agent runs under six context-clearing policies
How often should an LLM coding agent's context be cleared? This dataset holds every run behind the report
"Clear Every Third Task: A Measured U-Curve in the Context Economy of Coding Agents"
(Evgenii Arsentev, 2026; corrected version 1.2, DOI 10.5281/zenodo.22759217; version 1.0: DOI 10.5281/zenodo.22699668).
A fixed suite of twelve programming tasks was run under six session-length policies — a fresh… See the full description on the dataset page: https://huggingface.co/datasets/arsentev-ai/context-ucurve-coding-agents.sft-coding-agent-traces
My AI Coding Helper Data
I use this data to teach an AI how to code. I use records of past coding work.
What is in this data
This data has 6,625 examples. It has records from:
Real coding work.
Chat logs about code.
My own work with an AI helper.
How I use this data
I use this data to train a model. I use a method called Supervised Fine-Tuning. The AI learns how to think and how to use tools. It learns this by reading the examples in this data.… See the full description on the dataset page: https://huggingface.co/datasets/focustiki/sft-coding-agent-traces.agentic_coding_dataset
Agentic Coding Dataset
This dataset is a compilation of various coding and instruction-following datasets, designed to train agentic coding models.
Sources
This dataset aggregates samples from the following sources:
CodeAlpaca-20k
Instruction-following coding tasks.
Evol-CodeAlpaca-v1
Complex evolved coding instructions (WizardCoder style).
Code Review Instruct
Python code review, critique, and revision examples.
APPS (Automated Programming Progress Standard)… See the full description on the dataset page: https://huggingface.co/datasets/ethanker/agentic_coding_dataset.local-agentic-coding-bench-8gb-vram-2026-05
agentic coding benchmark: local LLMs on 8GB VRAM
can local LLMs do agentic coding (multi-turn tool calling, file creation, debugging) on consumer hardware? this dataset captures real test results.
hardware
GPU: NVIDIA RTX 4060 Ti 8GB
CPU: Intel i7-14700F
RAM: 32 GB DDR5
OS: Windows 11 + WSL2 (Ubuntu)
inference: llama-server (turboquant fork of llama.cpp)
what was tested
two agent frameworks:
Hermes Agent (NousResearch): structured tool calling with… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/local-agentic-coding-bench-8gb-vram-2026-05.coding-agent-security-benchmark
Coding Agent Security Benchmark
A benchmark for evaluating whether an LLM can correctly identify security
violations in the behavior of an autonomous coding agent - spanning
dangerous shell commands, credential leakage, prompt injection, supply-chain
risk, privacy leaks, and more.
Each row is a single message sampled from a coding-agent session (a user
instruction, a tool call the agent issued, a tool's response, or the agent's
own output) paired with a ground-truth security… See the full description on the dataset page: https://huggingface.co/datasets/ruchit11111/coding-agent-security-benchmark.Agentic-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/Agentic-Chain-of-Thought-Coding-SFT-Dataset.Hunter-Alpha-Coding-Agent-SFT
Hunter Alpha Coding Agent SFT
200 of the prompts for this dataset were sourced from MiniMaxAI/VIBE, and the rest were generated.
Each prompt was given to Hunter-Alpha (The stealth model recently revealed to be xiaomi/mimo-v2-pro) with the follow tools and system prompt:
read_file - Read file contents from workspace
write_file - Write content to a file
edit_file - Replace text in a file
list_directory - List files and directories
search_code - Search for patterns in files… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/Hunter-Alpha-Coding-Agent-SFT.ti_coding_agent_training_probe_20260624
Open-SWE-Traces Swift Probe 5K
Balanced 5,000-row training probe subset from nvidia/Open-SWE-Traces, exported for ModelScope SWIFT-style SFT.
Selection:
1,250 hard-filter-kept rows from each source config.
Original native scaffold semantics are preserved.
MiniMax rows are exported as thinking examples by wrapping reasoning_content in <think>...</think>.
Qwen rows are exported as non-thinking examples; reasoning_content is not emitted.
Tool responses are included as role: "tool"… See the full description on the dataset page: https://huggingface.co/datasets/eigentom/ti_coding_agent_training_probe_20260624.Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1.Coding-Agent-Github-2025-Feb
Coding Agent AI Agent Directory to Host All Coding Agent related AI Agents Web Traffic Data, Search Ranking, Community, Reviews and More.
This is the Coding Agent Dataset from pypi package "coding_agent" https://pypi.org/project/coding_agent. You can use this package to download and get statistics (forks/stars/website traffic) of AI agents on website from AI Agent Marketplace AI Agent Directory (http://www.deepnlp.org/store/ai-agent) and AI Agent Search Portal… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/Coding-Agent-Github-2025-Feb.tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1
Nemotron-Agentic-v1 — Cleaned & Rectified
335k multi-turn agentic tool-use trajectories from NVIDIA's Nemotron-Agentic-v1, converted into a strict reasoning + tool-call format with validated FSM transitions.
Origin
Derived from nvidia/Nemotron-Agentic-v1.
Nemotron-Agentic-v1 is a synthetic dataset of multi-turn conversations where language models decompose user goals, decide when to call tools, and reason over tool outputs. Trajectories are generated by simulating user… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1.agent-coding-traces-public
Description
Public agentic coding traces (Claude Code & Codex on SWE-bench-Pro / R2E-Gym), normalised to text/source/lang.
Derived dataset. Source material retains its original per-item licence (see source/repo columns); treat as other / mixed. Provided as-is.
Usage
from datasets import load_dataset
ds = load_dataset("PotatoHD/agent-coding-traces-public")
Agentic-Coding-Tessa
Agentic Coding Dataset for Tessa
A comprehensive dataset for training coding agents with tool-use, reasoning, and software engineering capabilities.
Dataset Composition
This dataset combines multiple high-quality sources:
hermes_reasoning (20.0%): Tool-use and reasoning dataset - interstellarninja/hermes_reasoning_tool_use
search_arena (15.0%): Search and retrieval tasks - lmarena-ai/search-arena-24k
arena_human_pref (15.0%): Human preference data for alignment -… See the full description on the dataset page: https://huggingface.co/datasets/smirki/Agentic-Coding-Tessa.tool-reasoning-sft-CODING-jupyter-agent-dataset-sft-tool-use-agent-data-cleaned-rectified
jupyter-agent-dataset-sft-tool-use-agent-data-cleaned-rectified
Multi-turn Jupyter notebook agent SFT dataset with explicit reasoning traces, structured tool calls, and stateful code execution chains.
Schema
Column
Type
Description
messages
list[struct{role, content}]
Native Arrow nested list of {role, content} dicts. Roles: system, user, reasoning, tool_call, tool_output, answer
Usage
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-jupyter-agent-dataset-sft-tool-use-agent-data-cleaned-rectified.Coding-Agent-Github-2025-Dec
Coding Agent AI Agent Directory to Host All Coding Agent related AI Agents Web Traffic Data, Search Ranking, Community, Reviews and More.
This is the Coding Agent Dataset from pypi package "coding_agent" https://pypi.org/project/coding_agent. You can use this package to download and get statistics (forks/stars/website traffic) of AI agents on website from AI Agent Marketplace AI Agent Directory (http://www.deepnlp.org/store/ai-agent) and AI Agent Search Portal… See the full description on the dataset page: https://huggingface.co/datasets/macmacmacmac/Coding-Agent-Github-2025-Dec.agentic-cot-coding-sft
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/agentic-cot-coding-sft.moe-coding-security-agent-v1agentic-coding-traces
Agentic Coding Mooncake Traces
Synthetic agentic coding benchmark datasets in Mooncake trace (JSONL) format,
generated with AIPerf 0.9.0 for LLM inference benchmarking.
Designed for use with InferenceX via the
agentic-replay scenario-type and aiperf_adapter.py.
Files
File
Sessions
Turns
max_prompt_tokens
Seed
64k/dataset.jsonl
1,000
18,595
65,536
42
128k/dataset.jsonl
1,000
16,957
131,072
42
Format
Each line is a Mooncake trace… See the full description on the dataset page: https://huggingface.co/datasets/thangquang09/agentic-coding-traces.coding-agent-synth-data